COVID-19_Analysis
Solomon
2024-08-10
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##In this report, R is used to import, tidy, transform, visualize, and model COVID-19 data. The details of the process are given in the steps below.
Step 1 Import R libraries and set up environment
library(plotly)## Warning: package 'plotly' was built under R version 4.3.3## Loading required package: ggplot2## Warning: package 'ggplot2' was built under R version 4.3.3##
## Attaching package: 'plotly'## The following object is masked from 'package:ggplot2':
##
## last_plot## The following object is masked from 'package:stats':
##
## filter## The following object is masked from 'package:graphics':
##
## layoutlibrary(tidyverse)## Warning: package 'tidyverse' was built under R version 4.3.3## Warning: package 'tidyr' was built under R version 4.3.3## Warning: package 'readr' was built under R version 4.3.3## Warning: package 'dplyr' was built under R version 4.3.3## Warning: package 'stringr' was built under R version 4.3.3## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ lubridate 1.9.3 ✔ tibble 3.2.1
## ✔ purrr 1.0.2 ✔ tidyr 1.3.1## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks plotly::filter(), stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(lubridate)
options(warn=-1)
options(dplyr.summarise.inform = FALSE)
knitr::opts_chunk$set(echo = TRUE)
knitr::opts_knit$set(root.dir = getwd())Step 2 Download and import COVID-19 source data files. Tidyverse package used to Read the CSV directly from the data sources.
url_in <-
'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/'
filenames <- c('time_series_covid19_confirmed_global.csv', 'time_series_covid19_deaths_global.csv', 'time_series_covid19_confirmed_US.csv', 'time_series_covid19_deaths_US.csv')
confirmed_global <- read_csv("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv")## Rows: 289 Columns: 1147
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): Province/State, Country/Region
## dbl (1145): Lat, Long, 1/22/20, 1/23/20, 1/24/20, 1/25/20, 1/26/20, 1/27/20,...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.deaths_global <- read_csv("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv")## Rows: 289 Columns: 1147
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): Province/State, Country/Region
## dbl (1145): Lat, Long, 1/22/20, 1/23/20, 1/24/20, 1/25/20, 1/26/20, 1/27/20,...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.confirmed_us <- read_csv("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_US.csv")## Rows: 3342 Columns: 1154
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (6): iso2, iso3, Admin2, Province_State, Country_Region, Combined_Key
## dbl (1148): UID, code3, FIPS, Lat, Long_, 1/22/20, 1/23/20, 1/24/20, 1/25/20...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.deaths_us <- read_csv("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_US.csv")## Rows: 3342 Columns: 1155
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (6): iso2, iso3, Admin2, Province_State, Country_Region, Combined_Key
## dbl (1149): UID, code3, FIPS, Lat, Long_, Population, 1/22/20, 1/23/20, 1/24...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.uid_lookup_url <-
'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/UID_ISO_FIPS_LookUp_Table.csv'
uid <- read_csv(uid_lookup_url) %>%
select(-c(Lat, Long_, Combined_Key, code3, iso2, iso3, Admin2))## Rows: 4321 Columns: 12
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (7): iso2, iso3, FIPS, Admin2, Province_State, Country_Region, Combined_Key
## dbl (5): UID, code3, Lat, Long_, Population
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.urls <- str_c(url_in, filenames)
global_cases <- read_csv(urls[1])## Rows: 289 Columns: 1147
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): Province/State, Country/Region
## dbl (1145): Lat, Long, 1/22/20, 1/23/20, 1/24/20, 1/25/20, 1/26/20, 1/27/20,...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.global_deaths <- read_csv(urls[2])## Rows: 289 Columns: 1147
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): Province/State, Country/Region
## dbl (1145): Lat, Long, 1/22/20, 1/23/20, 1/24/20, 1/25/20, 1/26/20, 1/27/20,...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.US_cases <- read_csv(urls[3])## Rows: 3342 Columns: 1154
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (6): iso2, iso3, Admin2, Province_State, Country_Region, Combined_Key
## dbl (1148): UID, code3, FIPS, Lat, Long_, 1/22/20, 1/23/20, 1/24/20, 1/25/20...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.US_deaths <- read_csv(urls[4])## Rows: 3342 Columns: 1155
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (6): iso2, iso3, Admin2, Province_State, Country_Region, Combined_Key
## dbl (1149): UID, code3, FIPS, Lat, Long_, Population, 1/22/20, 1/23/20, 1/24...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.# Read .csv file for US COVID-19 vaccinations
url_in <-
'https://covid.ourworldindata.org/data/vaccinations/us_state_vaccinations.csv'
US_vaccinations <- read_csv(url_in) %>%
select(-c(total_vaccinations, total_distributed, people_vaccinated, people_fully_vaccinated,
daily_vaccinations_raw, daily_vaccinations, daily_vaccinations_per_million,
share_doses_used, total_boosters)) %>%
rename(Province_State = 'location')## Rows: 54628 Columns: 16
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): location
## dbl (14): total_vaccinations, total_distributed, people_vaccinated, people_...
## date (1): date
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.head(confirmed_global)## # A tibble: 6 × 1,147
## `Province/State` `Country/Region` Lat Long `1/22/20` `1/23/20` `1/24/20`
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 <NA> Afghanistan 33.9 67.7 0 0 0
## 2 <NA> Albania 41.2 20.2 0 0 0
## 3 <NA> Algeria 28.0 1.66 0 0 0
## 4 <NA> Andorra 42.5 1.52 0 0 0
## 5 <NA> Angola -11.2 17.9 0 0 0
## 6 <NA> Antarctica -71.9 23.3 0 0 0
## # ℹ 1,140 more variables: `1/25/20` <dbl>, `1/26/20` <dbl>, `1/27/20` <dbl>,
## # `1/28/20` <dbl>, `1/29/20` <dbl>, `1/30/20` <dbl>, `1/31/20` <dbl>,
## # `2/1/20` <dbl>, `2/2/20` <dbl>, `2/3/20` <dbl>, `2/4/20` <dbl>,
## # `2/5/20` <dbl>, `2/6/20` <dbl>, `2/7/20` <dbl>, `2/8/20` <dbl>,
## # `2/9/20` <dbl>, `2/10/20` <dbl>, `2/11/20` <dbl>, `2/12/20` <dbl>,
## # `2/13/20` <dbl>, `2/14/20` <dbl>, `2/15/20` <dbl>, `2/16/20` <dbl>,
## # `2/17/20` <dbl>, `2/18/20` <dbl>, `2/19/20` <dbl>, `2/20/20` <dbl>, …sort(colnames(confirmed_global), decreasing = TRUE)## [1] "Province/State" "Long" "Lat" "Country/Region"
## [5] "9/9/22" "9/9/21" "9/9/20" "9/8/22"
## [9] "9/8/21" "9/8/20" "9/7/22" "9/7/21"
## [13] "9/7/20" "9/6/22" "9/6/21" "9/6/20"
## [17] "9/5/22" "9/5/21" "9/5/20" "9/4/22"
## [21] "9/4/21" "9/4/20" "9/30/22" "9/30/21"
## [25] "9/30/20" "9/3/22" "9/3/21" "9/3/20"
## [29] "9/29/22" "9/29/21" "9/29/20" "9/28/22"
## [33] "9/28/21" "9/28/20" "9/27/22" "9/27/21"
## [37] "9/27/20" "9/26/22" "9/26/21" "9/26/20"
## [41] "9/25/22" "9/25/21" "9/25/20" "9/24/22"
## [45] "9/24/21" "9/24/20" "9/23/22" "9/23/21"
## [49] "9/23/20" "9/22/22" "9/22/21" "9/22/20"
## [53] "9/21/22" "9/21/21" "9/21/20" "9/20/22"
## [57] "9/20/21" "9/20/20" "9/2/22" "9/2/21"
## [61] "9/2/20" "9/19/22" "9/19/21" "9/19/20"
## [65] "9/18/22" "9/18/21" "9/18/20" "9/17/22"
## [69] "9/17/21" "9/17/20" "9/16/22" "9/16/21"
## [73] "9/16/20" "9/15/22" "9/15/21" "9/15/20"
## [77] "9/14/22" "9/14/21" "9/14/20" "9/13/22"
## [81] "9/13/21" "9/13/20" "9/12/22" "9/12/21"
## [85] "9/12/20" "9/11/22" "9/11/21" "9/11/20"
## [89] "9/10/22" "9/10/21" "9/10/20" "9/1/22"
## [93] "9/1/21" "9/1/20" "8/9/22" "8/9/21"
## [97] "8/9/20" "8/8/22" "8/8/21" "8/8/20"
## [101] "8/7/22" "8/7/21" "8/7/20" "8/6/22"
## [105] "8/6/21" "8/6/20" "8/5/22" "8/5/21"
## [109] "8/5/20" "8/4/22" "8/4/21" "8/4/20"
## [113] "8/31/22" "8/31/21" "8/31/20" "8/30/22"
## [117] "8/30/21" "8/30/20" "8/3/22" "8/3/21"
## [121] "8/3/20" "8/29/22" "8/29/21" "8/29/20"
## [125] "8/28/22" "8/28/21" "8/28/20" "8/27/22"
## [129] "8/27/21" "8/27/20" "8/26/22" "8/26/21"
## [133] "8/26/20" "8/25/22" "8/25/21" "8/25/20"
## [137] "8/24/22" "8/24/21" "8/24/20" "8/23/22"
## [141] "8/23/21" "8/23/20" "8/22/22" "8/22/21"
## [145] "8/22/20" "8/21/22" "8/21/21" "8/21/20"
## [149] "8/20/22" "8/20/21" "8/20/20" "8/2/22"
## [153] "8/2/21" "8/2/20" "8/19/22" "8/19/21"
## [157] "8/19/20" "8/18/22" "8/18/21" "8/18/20"
## [161] "8/17/22" "8/17/21" "8/17/20" "8/16/22"
## [165] "8/16/21" "8/16/20" "8/15/22" "8/15/21"
## [169] "8/15/20" "8/14/22" "8/14/21" "8/14/20"
## [173] "8/13/22" "8/13/21" "8/13/20" "8/12/22"
## [177] "8/12/21" "8/12/20" "8/11/22" "8/11/21"
## [181] "8/11/20" "8/10/22" "8/10/21" "8/10/20"
## [185] "8/1/22" "8/1/21" "8/1/20" "7/9/22"
## [189] "7/9/21" "7/9/20" "7/8/22" "7/8/21"
## [193] "7/8/20" "7/7/22" "7/7/21" "7/7/20"
## [197] "7/6/22" "7/6/21" "7/6/20" "7/5/22"
## [201] "7/5/21" "7/5/20" "7/4/22" "7/4/21"
## [205] "7/4/20" "7/31/22" "7/31/21" "7/31/20"
## [209] "7/30/22" "7/30/21" "7/30/20" "7/3/22"
## [213] "7/3/21" "7/3/20" "7/29/22" "7/29/21"
## [217] "7/29/20" "7/28/22" "7/28/21" "7/28/20"
## [221] "7/27/22" "7/27/21" "7/27/20" "7/26/22"
## [225] "7/26/21" "7/26/20" "7/25/22" "7/25/21"
## [229] "7/25/20" "7/24/22" "7/24/21" "7/24/20"
## [233] "7/23/22" "7/23/21" "7/23/20" "7/22/22"
## [237] "7/22/21" "7/22/20" "7/21/22" "7/21/21"
## [241] "7/21/20" "7/20/22" "7/20/21" "7/20/20"
## [245] "7/2/22" "7/2/21" "7/2/20" "7/19/22"
## [249] "7/19/21" "7/19/20" "7/18/22" "7/18/21"
## [253] "7/18/20" "7/17/22" "7/17/21" "7/17/20"
## [257] "7/16/22" "7/16/21" "7/16/20" "7/15/22"
## [261] "7/15/21" "7/15/20" "7/14/22" "7/14/21"
## [265] "7/14/20" "7/13/22" "7/13/21" "7/13/20"
## [269] "7/12/22" "7/12/21" "7/12/20" "7/11/22"
## [273] "7/11/21" "7/11/20" "7/10/22" "7/10/21"
## [277] "7/10/20" "7/1/22" "7/1/21" "7/1/20"
## [281] "6/9/22" "6/9/21" "6/9/20" "6/8/22"
## [285] "6/8/21" "6/8/20" "6/7/22" "6/7/21"
## [289] "6/7/20" "6/6/22" "6/6/21" "6/6/20"
## [293] "6/5/22" "6/5/21" "6/5/20" "6/4/22"
## [297] "6/4/21" "6/4/20" "6/30/22" "6/30/21"
## [301] "6/30/20" "6/3/22" "6/3/21" "6/3/20"
## [305] "6/29/22" "6/29/21" "6/29/20" "6/28/22"
## [309] "6/28/21" "6/28/20" "6/27/22" "6/27/21"
## [313] "6/27/20" "6/26/22" "6/26/21" "6/26/20"
## [317] "6/25/22" "6/25/21" "6/25/20" "6/24/22"
## [321] "6/24/21" "6/24/20" "6/23/22" "6/23/21"
## [325] "6/23/20" "6/22/22" "6/22/21" "6/22/20"
## [329] "6/21/22" "6/21/21" "6/21/20" "6/20/22"
## [333] "6/20/21" "6/20/20" "6/2/22" "6/2/21"
## [337] "6/2/20" "6/19/22" "6/19/21" "6/19/20"
## [341] "6/18/22" "6/18/21" "6/18/20" "6/17/22"
## [345] "6/17/21" "6/17/20" "6/16/22" "6/16/21"
## [349] "6/16/20" "6/15/22" "6/15/21" "6/15/20"
## [353] "6/14/22" "6/14/21" "6/14/20" "6/13/22"
## [357] "6/13/21" "6/13/20" "6/12/22" "6/12/21"
## [361] "6/12/20" "6/11/22" "6/11/21" "6/11/20"
## [365] "6/10/22" "6/10/21" "6/10/20" "6/1/22"
## [369] "6/1/21" "6/1/20" "5/9/22" "5/9/21"
## [373] "5/9/20" "5/8/22" "5/8/21" "5/8/20"
## [377] "5/7/22" "5/7/21" "5/7/20" "5/6/22"
## [381] "5/6/21" "5/6/20" "5/5/22" "5/5/21"
## [385] "5/5/20" "5/4/22" "5/4/21" "5/4/20"
## [389] "5/31/22" "5/31/21" "5/31/20" "5/30/22"
## [393] "5/30/21" "5/30/20" "5/3/22" "5/3/21"
## [397] "5/3/20" "5/29/22" "5/29/21" "5/29/20"
## [401] "5/28/22" "5/28/21" "5/28/20" "5/27/22"
## [405] "5/27/21" "5/27/20" "5/26/22" "5/26/21"
## [409] "5/26/20" "5/25/22" "5/25/21" "5/25/20"
## [413] "5/24/22" "5/24/21" "5/24/20" "5/23/22"
## [417] "5/23/21" "5/23/20" "5/22/22" "5/22/21"
## [421] "5/22/20" "5/21/22" "5/21/21" "5/21/20"
## [425] "5/20/22" "5/20/21" "5/20/20" "5/2/22"
## [429] "5/2/21" "5/2/20" "5/19/22" "5/19/21"
## [433] "5/19/20" "5/18/22" "5/18/21" "5/18/20"
## [437] "5/17/22" "5/17/21" "5/17/20" "5/16/22"
## [441] "5/16/21" "5/16/20" "5/15/22" "5/15/21"
## [445] "5/15/20" "5/14/22" "5/14/21" "5/14/20"
## [449] "5/13/22" "5/13/21" "5/13/20" "5/12/22"
## [453] "5/12/21" "5/12/20" "5/11/22" "5/11/21"
## [457] "5/11/20" "5/10/22" "5/10/21" "5/10/20"
## [461] "5/1/22" "5/1/21" "5/1/20" "4/9/22"
## [465] "4/9/21" "4/9/20" "4/8/22" "4/8/21"
## [469] "4/8/20" "4/7/22" "4/7/21" "4/7/20"
## [473] "4/6/22" "4/6/21" "4/6/20" "4/5/22"
## [477] "4/5/21" "4/5/20" "4/4/22" "4/4/21"
## [481] "4/4/20" "4/30/22" "4/30/21" "4/30/20"
## [485] "4/3/22" "4/3/21" "4/3/20" "4/29/22"
## [489] "4/29/21" "4/29/20" "4/28/22" "4/28/21"
## [493] "4/28/20" "4/27/22" "4/27/21" "4/27/20"
## [497] "4/26/22" "4/26/21" "4/26/20" "4/25/22"
## [501] "4/25/21" "4/25/20" "4/24/22" "4/24/21"
## [505] "4/24/20" "4/23/22" "4/23/21" "4/23/20"
## [509] "4/22/22" "4/22/21" "4/22/20" "4/21/22"
## [513] "4/21/21" "4/21/20" "4/20/22" "4/20/21"
## [517] "4/20/20" "4/2/22" "4/2/21" "4/2/20"
## [521] "4/19/22" "4/19/21" "4/19/20" "4/18/22"
## [525] "4/18/21" "4/18/20" "4/17/22" "4/17/21"
## [529] "4/17/20" "4/16/22" "4/16/21" "4/16/20"
## [533] "4/15/22" "4/15/21" "4/15/20" "4/14/22"
## [537] "4/14/21" "4/14/20" "4/13/22" "4/13/21"
## [541] "4/13/20" "4/12/22" "4/12/21" "4/12/20"
## [545] "4/11/22" "4/11/21" "4/11/20" "4/10/22"
## [549] "4/10/21" "4/10/20" "4/1/22" "4/1/21"
## [553] "4/1/20" "3/9/23" "3/9/22" "3/9/21"
## [557] "3/9/20" "3/8/23" "3/8/22" "3/8/21"
## [561] "3/8/20" "3/7/23" "3/7/22" "3/7/21"
## [565] "3/7/20" "3/6/23" "3/6/22" "3/6/21"
## [569] "3/6/20" "3/5/23" "3/5/22" "3/5/21"
## [573] "3/5/20" "3/4/23" "3/4/22" "3/4/21"
## [577] "3/4/20" "3/31/22" "3/31/21" "3/31/20"
## [581] "3/30/22" "3/30/21" "3/30/20" "3/3/23"
## [585] "3/3/22" "3/3/21" "3/3/20" "3/29/22"
## [589] "3/29/21" "3/29/20" "3/28/22" "3/28/21"
## [593] "3/28/20" "3/27/22" "3/27/21" "3/27/20"
## [597] "3/26/22" "3/26/21" "3/26/20" "3/25/22"
## [601] "3/25/21" "3/25/20" "3/24/22" "3/24/21"
## [605] "3/24/20" "3/23/22" "3/23/21" "3/23/20"
## [609] "3/22/22" "3/22/21" "3/22/20" "3/21/22"
## [613] "3/21/21" "3/21/20" "3/20/22" "3/20/21"
## [617] "3/20/20" "3/2/23" "3/2/22" "3/2/21"
## [621] "3/2/20" "3/19/22" "3/19/21" "3/19/20"
## [625] "3/18/22" "3/18/21" "3/18/20" "3/17/22"
## [629] "3/17/21" "3/17/20" "3/16/22" "3/16/21"
## [633] "3/16/20" "3/15/22" "3/15/21" "3/15/20"
## [637] "3/14/22" "3/14/21" "3/14/20" "3/13/22"
## [641] "3/13/21" "3/13/20" "3/12/22" "3/12/21"
## [645] "3/12/20" "3/11/22" "3/11/21" "3/11/20"
## [649] "3/10/22" "3/10/21" "3/10/20" "3/1/23"
## [653] "3/1/22" "3/1/21" "3/1/20" "2/9/23"
## [657] "2/9/22" "2/9/21" "2/9/20" "2/8/23"
## [661] "2/8/22" "2/8/21" "2/8/20" "2/7/23"
## [665] "2/7/22" "2/7/21" "2/7/20" "2/6/23"
## [669] "2/6/22" "2/6/21" "2/6/20" "2/5/23"
## [673] "2/5/22" "2/5/21" "2/5/20" "2/4/23"
## [677] "2/4/22" "2/4/21" "2/4/20" "2/3/23"
## [681] "2/3/22" "2/3/21" "2/3/20" "2/29/20"
## [685] "2/28/23" "2/28/22" "2/28/21" "2/28/20"
## [689] "2/27/23" "2/27/22" "2/27/21" "2/27/20"
## [693] "2/26/23" "2/26/22" "2/26/21" "2/26/20"
## [697] "2/25/23" "2/25/22" "2/25/21" "2/25/20"
## [701] "2/24/23" "2/24/22" "2/24/21" "2/24/20"
## [705] "2/23/23" "2/23/22" "2/23/21" "2/23/20"
## [709] "2/22/23" "2/22/22" "2/22/21" "2/22/20"
## [713] "2/21/23" "2/21/22" "2/21/21" "2/21/20"
## [717] "2/20/23" "2/20/22" "2/20/21" "2/20/20"
## [721] "2/2/23" "2/2/22" "2/2/21" "2/2/20"
## [725] "2/19/23" "2/19/22" "2/19/21" "2/19/20"
## [729] "2/18/23" "2/18/22" "2/18/21" "2/18/20"
## [733] "2/17/23" "2/17/22" "2/17/21" "2/17/20"
## [737] "2/16/23" "2/16/22" "2/16/21" "2/16/20"
## [741] "2/15/23" "2/15/22" "2/15/21" "2/15/20"
## [745] "2/14/23" "2/14/22" "2/14/21" "2/14/20"
## [749] "2/13/23" "2/13/22" "2/13/21" "2/13/20"
## [753] "2/12/23" "2/12/22" "2/12/21" "2/12/20"
## [757] "2/11/23" "2/11/22" "2/11/21" "2/11/20"
## [761] "2/10/23" "2/10/22" "2/10/21" "2/10/20"
## [765] "2/1/23" "2/1/22" "2/1/21" "2/1/20"
## [769] "12/9/22" "12/9/21" "12/9/20" "12/8/22"
## [773] "12/8/21" "12/8/20" "12/7/22" "12/7/21"
## [777] "12/7/20" "12/6/22" "12/6/21" "12/6/20"
## [781] "12/5/22" "12/5/21" "12/5/20" "12/4/22"
## [785] "12/4/21" "12/4/20" "12/31/22" "12/31/21"
## [789] "12/31/20" "12/30/22" "12/30/21" "12/30/20"
## [793] "12/3/22" "12/3/21" "12/3/20" "12/29/22"
## [797] "12/29/21" "12/29/20" "12/28/22" "12/28/21"
## [801] "12/28/20" "12/27/22" "12/27/21" "12/27/20"
## [805] "12/26/22" "12/26/21" "12/26/20" "12/25/22"
## [809] "12/25/21" "12/25/20" "12/24/22" "12/24/21"
## [813] "12/24/20" "12/23/22" "12/23/21" "12/23/20"
## [817] "12/22/22" "12/22/21" "12/22/20" "12/21/22"
## [821] "12/21/21" "12/21/20" "12/20/22" "12/20/21"
## [825] "12/20/20" "12/2/22" "12/2/21" "12/2/20"
## [829] "12/19/22" "12/19/21" "12/19/20" "12/18/22"
## [833] "12/18/21" "12/18/20" "12/17/22" "12/17/21"
## [837] "12/17/20" "12/16/22" "12/16/21" "12/16/20"
## [841] "12/15/22" "12/15/21" "12/15/20" "12/14/22"
## [845] "12/14/21" "12/14/20" "12/13/22" "12/13/21"
## [849] "12/13/20" "12/12/22" "12/12/21" "12/12/20"
## [853] "12/11/22" "12/11/21" "12/11/20" "12/10/22"
## [857] "12/10/21" "12/10/20" "12/1/22" "12/1/21"
## [861] "12/1/20" "11/9/22" "11/9/21" "11/9/20"
## [865] "11/8/22" "11/8/21" "11/8/20" "11/7/22"
## [869] "11/7/21" "11/7/20" "11/6/22" "11/6/21"
## [873] "11/6/20" "11/5/22" "11/5/21" "11/5/20"
## [877] "11/4/22" "11/4/21" "11/4/20" "11/30/22"
## [881] "11/30/21" "11/30/20" "11/3/22" "11/3/21"
## [885] "11/3/20" "11/29/22" "11/29/21" "11/29/20"
## [889] "11/28/22" "11/28/21" "11/28/20" "11/27/22"
## [893] "11/27/21" "11/27/20" "11/26/22" "11/26/21"
## [897] "11/26/20" "11/25/22" "11/25/21" "11/25/20"
## [901] "11/24/22" "11/24/21" "11/24/20" "11/23/22"
## [905] "11/23/21" "11/23/20" "11/22/22" "11/22/21"
## [909] "11/22/20" "11/21/22" "11/21/21" "11/21/20"
## [913] "11/20/22" "11/20/21" "11/20/20" "11/2/22"
## [917] "11/2/21" "11/2/20" "11/19/22" "11/19/21"
## [921] "11/19/20" "11/18/22" "11/18/21" "11/18/20"
## [925] "11/17/22" "11/17/21" "11/17/20" "11/16/22"
## [929] "11/16/21" "11/16/20" "11/15/22" "11/15/21"
## [933] "11/15/20" "11/14/22" "11/14/21" "11/14/20"
## [937] "11/13/22" "11/13/21" "11/13/20" "11/12/22"
## [941] "11/12/21" "11/12/20" "11/11/22" "11/11/21"
## [945] "11/11/20" "11/10/22" "11/10/21" "11/10/20"
## [949] "11/1/22" "11/1/21" "11/1/20" "10/9/22"
## [953] "10/9/21" "10/9/20" "10/8/22" "10/8/21"
## [957] "10/8/20" "10/7/22" "10/7/21" "10/7/20"
## [961] "10/6/22" "10/6/21" "10/6/20" "10/5/22"
## [965] "10/5/21" "10/5/20" "10/4/22" "10/4/21"
## [969] "10/4/20" "10/31/22" "10/31/21" "10/31/20"
## [973] "10/30/22" "10/30/21" "10/30/20" "10/3/22"
## [977] "10/3/21" "10/3/20" "10/29/22" "10/29/21"
## [981] "10/29/20" "10/28/22" "10/28/21" "10/28/20"
## [985] "10/27/22" "10/27/21" "10/27/20" "10/26/22"
## [989] "10/26/21" "10/26/20" "10/25/22" "10/25/21"
## [993] "10/25/20" "10/24/22" "10/24/21" "10/24/20"
## [997] "10/23/22" "10/23/21" "10/23/20" "10/22/22"
## [1001] "10/22/21" "10/22/20" "10/21/22" "10/21/21"
## [1005] "10/21/20" "10/20/22" "10/20/21" "10/20/20"
## [1009] "10/2/22" "10/2/21" "10/2/20" "10/19/22"
## [1013] "10/19/21" "10/19/20" "10/18/22" "10/18/21"
## [1017] "10/18/20" "10/17/22" "10/17/21" "10/17/20"
## [1021] "10/16/22" "10/16/21" "10/16/20" "10/15/22"
## [1025] "10/15/21" "10/15/20" "10/14/22" "10/14/21"
## [1029] "10/14/20" "10/13/22" "10/13/21" "10/13/20"
## [1033] "10/12/22" "10/12/21" "10/12/20" "10/11/22"
## [1037] "10/11/21" "10/11/20" "10/10/22" "10/10/21"
## [1041] "10/10/20" "10/1/22" "10/1/21" "10/1/20"
## [1045] "1/9/23" "1/9/22" "1/9/21" "1/8/23"
## [1049] "1/8/22" "1/8/21" "1/7/23" "1/7/22"
## [1053] "1/7/21" "1/6/23" "1/6/22" "1/6/21"
## [1057] "1/5/23" "1/5/22" "1/5/21" "1/4/23"
## [1061] "1/4/22" "1/4/21" "1/31/23" "1/31/22"
## [1065] "1/31/21" "1/31/20" "1/30/23" "1/30/22"
## [1069] "1/30/21" "1/30/20" "1/3/23" "1/3/22"
## [1073] "1/3/21" "1/29/23" "1/29/22" "1/29/21"
## [1077] "1/29/20" "1/28/23" "1/28/22" "1/28/21"
## [1081] "1/28/20" "1/27/23" "1/27/22" "1/27/21"
## [1085] "1/27/20" "1/26/23" "1/26/22" "1/26/21"
## [1089] "1/26/20" "1/25/23" "1/25/22" "1/25/21"
## [1093] "1/25/20" "1/24/23" "1/24/22" "1/24/21"
## [1097] "1/24/20" "1/23/23" "1/23/22" "1/23/21"
## [1101] "1/23/20" "1/22/23" "1/22/22" "1/22/21"
## [1105] "1/22/20" "1/21/23" "1/21/22" "1/21/21"
## [1109] "1/20/23" "1/20/22" "1/20/21" "1/2/23"
## [1113] "1/2/22" "1/2/21" "1/19/23" "1/19/22"
## [1117] "1/19/21" "1/18/23" "1/18/22" "1/18/21"
## [1121] "1/17/23" "1/17/22" "1/17/21" "1/16/23"
## [1125] "1/16/22" "1/16/21" "1/15/23" "1/15/22"
## [1129] "1/15/21" "1/14/23" "1/14/22" "1/14/21"
## [1133] "1/13/23" "1/13/22" "1/13/21" "1/12/23"
## [1137] "1/12/22" "1/12/21" "1/11/23" "1/11/22"
## [1141] "1/11/21" "1/10/23" "1/10/22" "1/10/21"
## [1145] "1/1/23" "1/1/22" "1/1/21"head(deaths_global)## # A tibble: 6 × 1,147
## `Province/State` `Country/Region` Lat Long `1/22/20` `1/23/20` `1/24/20`
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 <NA> Afghanistan 33.9 67.7 0 0 0
## 2 <NA> Albania 41.2 20.2 0 0 0
## 3 <NA> Algeria 28.0 1.66 0 0 0
## 4 <NA> Andorra 42.5 1.52 0 0 0
## 5 <NA> Angola -11.2 17.9 0 0 0
## 6 <NA> Antarctica -71.9 23.3 0 0 0
## # ℹ 1,140 more variables: `1/25/20` <dbl>, `1/26/20` <dbl>, `1/27/20` <dbl>,
## # `1/28/20` <dbl>, `1/29/20` <dbl>, `1/30/20` <dbl>, `1/31/20` <dbl>,
## # `2/1/20` <dbl>, `2/2/20` <dbl>, `2/3/20` <dbl>, `2/4/20` <dbl>,
## # `2/5/20` <dbl>, `2/6/20` <dbl>, `2/7/20` <dbl>, `2/8/20` <dbl>,
## # `2/9/20` <dbl>, `2/10/20` <dbl>, `2/11/20` <dbl>, `2/12/20` <dbl>,
## # `2/13/20` <dbl>, `2/14/20` <dbl>, `2/15/20` <dbl>, `2/16/20` <dbl>,
## # `2/17/20` <dbl>, `2/18/20` <dbl>, `2/19/20` <dbl>, `2/20/20` <dbl>, …sort(colnames(deaths_global), decreasing = TRUE)## [1] "Province/State" "Long" "Lat" "Country/Region"
## [5] "9/9/22" "9/9/21" "9/9/20" "9/8/22"
## [9] "9/8/21" "9/8/20" "9/7/22" "9/7/21"
## [13] "9/7/20" "9/6/22" "9/6/21" "9/6/20"
## [17] "9/5/22" "9/5/21" "9/5/20" "9/4/22"
## [21] "9/4/21" "9/4/20" "9/30/22" "9/30/21"
## [25] "9/30/20" "9/3/22" "9/3/21" "9/3/20"
## [29] "9/29/22" "9/29/21" "9/29/20" "9/28/22"
## [33] "9/28/21" "9/28/20" "9/27/22" "9/27/21"
## [37] "9/27/20" "9/26/22" "9/26/21" "9/26/20"
## [41] "9/25/22" "9/25/21" "9/25/20" "9/24/22"
## [45] "9/24/21" "9/24/20" "9/23/22" "9/23/21"
## [49] "9/23/20" "9/22/22" "9/22/21" "9/22/20"
## [53] "9/21/22" "9/21/21" "9/21/20" "9/20/22"
## [57] "9/20/21" "9/20/20" "9/2/22" "9/2/21"
## [61] "9/2/20" "9/19/22" "9/19/21" "9/19/20"
## [65] "9/18/22" "9/18/21" "9/18/20" "9/17/22"
## [69] "9/17/21" "9/17/20" "9/16/22" "9/16/21"
## [73] "9/16/20" "9/15/22" "9/15/21" "9/15/20"
## [77] "9/14/22" "9/14/21" "9/14/20" "9/13/22"
## [81] "9/13/21" "9/13/20" "9/12/22" "9/12/21"
## [85] "9/12/20" "9/11/22" "9/11/21" "9/11/20"
## [89] "9/10/22" "9/10/21" "9/10/20" "9/1/22"
## [93] "9/1/21" "9/1/20" "8/9/22" "8/9/21"
## [97] "8/9/20" "8/8/22" "8/8/21" "8/8/20"
## [101] "8/7/22" "8/7/21" "8/7/20" "8/6/22"
## [105] "8/6/21" "8/6/20" "8/5/22" "8/5/21"
## [109] "8/5/20" "8/4/22" "8/4/21" "8/4/20"
## [113] "8/31/22" "8/31/21" "8/31/20" "8/30/22"
## [117] "8/30/21" "8/30/20" "8/3/22" "8/3/21"
## [121] "8/3/20" "8/29/22" "8/29/21" "8/29/20"
## [125] "8/28/22" "8/28/21" "8/28/20" "8/27/22"
## [129] "8/27/21" "8/27/20" "8/26/22" "8/26/21"
## [133] "8/26/20" "8/25/22" "8/25/21" "8/25/20"
## [137] "8/24/22" "8/24/21" "8/24/20" "8/23/22"
## [141] "8/23/21" "8/23/20" "8/22/22" "8/22/21"
## [145] "8/22/20" "8/21/22" "8/21/21" "8/21/20"
## [149] "8/20/22" "8/20/21" "8/20/20" "8/2/22"
## [153] "8/2/21" "8/2/20" "8/19/22" "8/19/21"
## [157] "8/19/20" "8/18/22" "8/18/21" "8/18/20"
## [161] "8/17/22" "8/17/21" "8/17/20" "8/16/22"
## [165] "8/16/21" "8/16/20" "8/15/22" "8/15/21"
## [169] "8/15/20" "8/14/22" "8/14/21" "8/14/20"
## [173] "8/13/22" "8/13/21" "8/13/20" "8/12/22"
## [177] "8/12/21" "8/12/20" "8/11/22" "8/11/21"
## [181] "8/11/20" "8/10/22" "8/10/21" "8/10/20"
## [185] "8/1/22" "8/1/21" "8/1/20" "7/9/22"
## [189] "7/9/21" "7/9/20" "7/8/22" "7/8/21"
## [193] "7/8/20" "7/7/22" "7/7/21" "7/7/20"
## [197] "7/6/22" "7/6/21" "7/6/20" "7/5/22"
## [201] "7/5/21" "7/5/20" "7/4/22" "7/4/21"
## [205] "7/4/20" "7/31/22" "7/31/21" "7/31/20"
## [209] "7/30/22" "7/30/21" "7/30/20" "7/3/22"
## [213] "7/3/21" "7/3/20" "7/29/22" "7/29/21"
## [217] "7/29/20" "7/28/22" "7/28/21" "7/28/20"
## [221] "7/27/22" "7/27/21" "7/27/20" "7/26/22"
## [225] "7/26/21" "7/26/20" "7/25/22" "7/25/21"
## [229] "7/25/20" "7/24/22" "7/24/21" "7/24/20"
## [233] "7/23/22" "7/23/21" "7/23/20" "7/22/22"
## [237] "7/22/21" "7/22/20" "7/21/22" "7/21/21"
## [241] "7/21/20" "7/20/22" "7/20/21" "7/20/20"
## [245] "7/2/22" "7/2/21" "7/2/20" "7/19/22"
## [249] "7/19/21" "7/19/20" "7/18/22" "7/18/21"
## [253] "7/18/20" "7/17/22" "7/17/21" "7/17/20"
## [257] "7/16/22" "7/16/21" "7/16/20" "7/15/22"
## [261] "7/15/21" "7/15/20" "7/14/22" "7/14/21"
## [265] "7/14/20" "7/13/22" "7/13/21" "7/13/20"
## [269] "7/12/22" "7/12/21" "7/12/20" "7/11/22"
## [273] "7/11/21" "7/11/20" "7/10/22" "7/10/21"
## [277] "7/10/20" "7/1/22" "7/1/21" "7/1/20"
## [281] "6/9/22" "6/9/21" "6/9/20" "6/8/22"
## [285] "6/8/21" "6/8/20" "6/7/22" "6/7/21"
## [289] "6/7/20" "6/6/22" "6/6/21" "6/6/20"
## [293] "6/5/22" "6/5/21" "6/5/20" "6/4/22"
## [297] "6/4/21" "6/4/20" "6/30/22" "6/30/21"
## [301] "6/30/20" "6/3/22" "6/3/21" "6/3/20"
## [305] "6/29/22" "6/29/21" "6/29/20" "6/28/22"
## [309] "6/28/21" "6/28/20" "6/27/22" "6/27/21"
## [313] "6/27/20" "6/26/22" "6/26/21" "6/26/20"
## [317] "6/25/22" "6/25/21" "6/25/20" "6/24/22"
## [321] "6/24/21" "6/24/20" "6/23/22" "6/23/21"
## [325] "6/23/20" "6/22/22" "6/22/21" "6/22/20"
## [329] "6/21/22" "6/21/21" "6/21/20" "6/20/22"
## [333] "6/20/21" "6/20/20" "6/2/22" "6/2/21"
## [337] "6/2/20" "6/19/22" "6/19/21" "6/19/20"
## [341] "6/18/22" "6/18/21" "6/18/20" "6/17/22"
## [345] "6/17/21" "6/17/20" "6/16/22" "6/16/21"
## [349] "6/16/20" "6/15/22" "6/15/21" "6/15/20"
## [353] "6/14/22" "6/14/21" "6/14/20" "6/13/22"
## [357] "6/13/21" "6/13/20" "6/12/22" "6/12/21"
## [361] "6/12/20" "6/11/22" "6/11/21" "6/11/20"
## [365] "6/10/22" "6/10/21" "6/10/20" "6/1/22"
## [369] "6/1/21" "6/1/20" "5/9/22" "5/9/21"
## [373] "5/9/20" "5/8/22" "5/8/21" "5/8/20"
## [377] "5/7/22" "5/7/21" "5/7/20" "5/6/22"
## [381] "5/6/21" "5/6/20" "5/5/22" "5/5/21"
## [385] "5/5/20" "5/4/22" "5/4/21" "5/4/20"
## [389] "5/31/22" "5/31/21" "5/31/20" "5/30/22"
## [393] "5/30/21" "5/30/20" "5/3/22" "5/3/21"
## [397] "5/3/20" "5/29/22" "5/29/21" "5/29/20"
## [401] "5/28/22" "5/28/21" "5/28/20" "5/27/22"
## [405] "5/27/21" "5/27/20" "5/26/22" "5/26/21"
## [409] "5/26/20" "5/25/22" "5/25/21" "5/25/20"
## [413] "5/24/22" "5/24/21" "5/24/20" "5/23/22"
## [417] "5/23/21" "5/23/20" "5/22/22" "5/22/21"
## [421] "5/22/20" "5/21/22" "5/21/21" "5/21/20"
## [425] "5/20/22" "5/20/21" "5/20/20" "5/2/22"
## [429] "5/2/21" "5/2/20" "5/19/22" "5/19/21"
## [433] "5/19/20" "5/18/22" "5/18/21" "5/18/20"
## [437] "5/17/22" "5/17/21" "5/17/20" "5/16/22"
## [441] "5/16/21" "5/16/20" "5/15/22" "5/15/21"
## [445] "5/15/20" "5/14/22" "5/14/21" "5/14/20"
## [449] "5/13/22" "5/13/21" "5/13/20" "5/12/22"
## [453] "5/12/21" "5/12/20" "5/11/22" "5/11/21"
## [457] "5/11/20" "5/10/22" "5/10/21" "5/10/20"
## [461] "5/1/22" "5/1/21" "5/1/20" "4/9/22"
## [465] "4/9/21" "4/9/20" "4/8/22" "4/8/21"
## [469] "4/8/20" "4/7/22" "4/7/21" "4/7/20"
## [473] "4/6/22" "4/6/21" "4/6/20" "4/5/22"
## [477] "4/5/21" "4/5/20" "4/4/22" "4/4/21"
## [481] "4/4/20" "4/30/22" "4/30/21" "4/30/20"
## [485] "4/3/22" "4/3/21" "4/3/20" "4/29/22"
## [489] "4/29/21" "4/29/20" "4/28/22" "4/28/21"
## [493] "4/28/20" "4/27/22" "4/27/21" "4/27/20"
## [497] "4/26/22" "4/26/21" "4/26/20" "4/25/22"
## [501] "4/25/21" "4/25/20" "4/24/22" "4/24/21"
## [505] "4/24/20" "4/23/22" "4/23/21" "4/23/20"
## [509] "4/22/22" "4/22/21" "4/22/20" "4/21/22"
## [513] "4/21/21" "4/21/20" "4/20/22" "4/20/21"
## [517] "4/20/20" "4/2/22" "4/2/21" "4/2/20"
## [521] "4/19/22" "4/19/21" "4/19/20" "4/18/22"
## [525] "4/18/21" "4/18/20" "4/17/22" "4/17/21"
## [529] "4/17/20" "4/16/22" "4/16/21" "4/16/20"
## [533] "4/15/22" "4/15/21" "4/15/20" "4/14/22"
## [537] "4/14/21" "4/14/20" "4/13/22" "4/13/21"
## [541] "4/13/20" "4/12/22" "4/12/21" "4/12/20"
## [545] "4/11/22" "4/11/21" "4/11/20" "4/10/22"
## [549] "4/10/21" "4/10/20" "4/1/22" "4/1/21"
## [553] "4/1/20" "3/9/23" "3/9/22" "3/9/21"
## [557] "3/9/20" "3/8/23" "3/8/22" "3/8/21"
## [561] "3/8/20" "3/7/23" "3/7/22" "3/7/21"
## [565] "3/7/20" "3/6/23" "3/6/22" "3/6/21"
## [569] "3/6/20" "3/5/23" "3/5/22" "3/5/21"
## [573] "3/5/20" "3/4/23" "3/4/22" "3/4/21"
## [577] "3/4/20" "3/31/22" "3/31/21" "3/31/20"
## [581] "3/30/22" "3/30/21" "3/30/20" "3/3/23"
## [585] "3/3/22" "3/3/21" "3/3/20" "3/29/22"
## [589] "3/29/21" "3/29/20" "3/28/22" "3/28/21"
## [593] "3/28/20" "3/27/22" "3/27/21" "3/27/20"
## [597] "3/26/22" "3/26/21" "3/26/20" "3/25/22"
## [601] "3/25/21" "3/25/20" "3/24/22" "3/24/21"
## [605] "3/24/20" "3/23/22" "3/23/21" "3/23/20"
## [609] "3/22/22" "3/22/21" "3/22/20" "3/21/22"
## [613] "3/21/21" "3/21/20" "3/20/22" "3/20/21"
## [617] "3/20/20" "3/2/23" "3/2/22" "3/2/21"
## [621] "3/2/20" "3/19/22" "3/19/21" "3/19/20"
## [625] "3/18/22" "3/18/21" "3/18/20" "3/17/22"
## [629] "3/17/21" "3/17/20" "3/16/22" "3/16/21"
## [633] "3/16/20" "3/15/22" "3/15/21" "3/15/20"
## [637] "3/14/22" "3/14/21" "3/14/20" "3/13/22"
## [641] "3/13/21" "3/13/20" "3/12/22" "3/12/21"
## [645] "3/12/20" "3/11/22" "3/11/21" "3/11/20"
## [649] "3/10/22" "3/10/21" "3/10/20" "3/1/23"
## [653] "3/1/22" "3/1/21" "3/1/20" "2/9/23"
## [657] "2/9/22" "2/9/21" "2/9/20" "2/8/23"
## [661] "2/8/22" "2/8/21" "2/8/20" "2/7/23"
## [665] "2/7/22" "2/7/21" "2/7/20" "2/6/23"
## [669] "2/6/22" "2/6/21" "2/6/20" "2/5/23"
## [673] "2/5/22" "2/5/21" "2/5/20" "2/4/23"
## [677] "2/4/22" "2/4/21" "2/4/20" "2/3/23"
## [681] "2/3/22" "2/3/21" "2/3/20" "2/29/20"
## [685] "2/28/23" "2/28/22" "2/28/21" "2/28/20"
## [689] "2/27/23" "2/27/22" "2/27/21" "2/27/20"
## [693] "2/26/23" "2/26/22" "2/26/21" "2/26/20"
## [697] "2/25/23" "2/25/22" "2/25/21" "2/25/20"
## [701] "2/24/23" "2/24/22" "2/24/21" "2/24/20"
## [705] "2/23/23" "2/23/22" "2/23/21" "2/23/20"
## [709] "2/22/23" "2/22/22" "2/22/21" "2/22/20"
## [713] "2/21/23" "2/21/22" "2/21/21" "2/21/20"
## [717] "2/20/23" "2/20/22" "2/20/21" "2/20/20"
## [721] "2/2/23" "2/2/22" "2/2/21" "2/2/20"
## [725] "2/19/23" "2/19/22" "2/19/21" "2/19/20"
## [729] "2/18/23" "2/18/22" "2/18/21" "2/18/20"
## [733] "2/17/23" "2/17/22" "2/17/21" "2/17/20"
## [737] "2/16/23" "2/16/22" "2/16/21" "2/16/20"
## [741] "2/15/23" "2/15/22" "2/15/21" "2/15/20"
## [745] "2/14/23" "2/14/22" "2/14/21" "2/14/20"
## [749] "2/13/23" "2/13/22" "2/13/21" "2/13/20"
## [753] "2/12/23" "2/12/22" "2/12/21" "2/12/20"
## [757] "2/11/23" "2/11/22" "2/11/21" "2/11/20"
## [761] "2/10/23" "2/10/22" "2/10/21" "2/10/20"
## [765] "2/1/23" "2/1/22" "2/1/21" "2/1/20"
## [769] "12/9/22" "12/9/21" "12/9/20" "12/8/22"
## [773] "12/8/21" "12/8/20" "12/7/22" "12/7/21"
## [777] "12/7/20" "12/6/22" "12/6/21" "12/6/20"
## [781] "12/5/22" "12/5/21" "12/5/20" "12/4/22"
## [785] "12/4/21" "12/4/20" "12/31/22" "12/31/21"
## [789] "12/31/20" "12/30/22" "12/30/21" "12/30/20"
## [793] "12/3/22" "12/3/21" "12/3/20" "12/29/22"
## [797] "12/29/21" "12/29/20" "12/28/22" "12/28/21"
## [801] "12/28/20" "12/27/22" "12/27/21" "12/27/20"
## [805] "12/26/22" "12/26/21" "12/26/20" "12/25/22"
## [809] "12/25/21" "12/25/20" "12/24/22" "12/24/21"
## [813] "12/24/20" "12/23/22" "12/23/21" "12/23/20"
## [817] "12/22/22" "12/22/21" "12/22/20" "12/21/22"
## [821] "12/21/21" "12/21/20" "12/20/22" "12/20/21"
## [825] "12/20/20" "12/2/22" "12/2/21" "12/2/20"
## [829] "12/19/22" "12/19/21" "12/19/20" "12/18/22"
## [833] "12/18/21" "12/18/20" "12/17/22" "12/17/21"
## [837] "12/17/20" "12/16/22" "12/16/21" "12/16/20"
## [841] "12/15/22" "12/15/21" "12/15/20" "12/14/22"
## [845] "12/14/21" "12/14/20" "12/13/22" "12/13/21"
## [849] "12/13/20" "12/12/22" "12/12/21" "12/12/20"
## [853] "12/11/22" "12/11/21" "12/11/20" "12/10/22"
## [857] "12/10/21" "12/10/20" "12/1/22" "12/1/21"
## [861] "12/1/20" "11/9/22" "11/9/21" "11/9/20"
## [865] "11/8/22" "11/8/21" "11/8/20" "11/7/22"
## [869] "11/7/21" "11/7/20" "11/6/22" "11/6/21"
## [873] "11/6/20" "11/5/22" "11/5/21" "11/5/20"
## [877] "11/4/22" "11/4/21" "11/4/20" "11/30/22"
## [881] "11/30/21" "11/30/20" "11/3/22" "11/3/21"
## [885] "11/3/20" "11/29/22" "11/29/21" "11/29/20"
## [889] "11/28/22" "11/28/21" "11/28/20" "11/27/22"
## [893] "11/27/21" "11/27/20" "11/26/22" "11/26/21"
## [897] "11/26/20" "11/25/22" "11/25/21" "11/25/20"
## [901] "11/24/22" "11/24/21" "11/24/20" "11/23/22"
## [905] "11/23/21" "11/23/20" "11/22/22" "11/22/21"
## [909] "11/22/20" "11/21/22" "11/21/21" "11/21/20"
## [913] "11/20/22" "11/20/21" "11/20/20" "11/2/22"
## [917] "11/2/21" "11/2/20" "11/19/22" "11/19/21"
## [921] "11/19/20" "11/18/22" "11/18/21" "11/18/20"
## [925] "11/17/22" "11/17/21" "11/17/20" "11/16/22"
## [929] "11/16/21" "11/16/20" "11/15/22" "11/15/21"
## [933] "11/15/20" "11/14/22" "11/14/21" "11/14/20"
## [937] "11/13/22" "11/13/21" "11/13/20" "11/12/22"
## [941] "11/12/21" "11/12/20" "11/11/22" "11/11/21"
## [945] "11/11/20" "11/10/22" "11/10/21" "11/10/20"
## [949] "11/1/22" "11/1/21" "11/1/20" "10/9/22"
## [953] "10/9/21" "10/9/20" "10/8/22" "10/8/21"
## [957] "10/8/20" "10/7/22" "10/7/21" "10/7/20"
## [961] "10/6/22" "10/6/21" "10/6/20" "10/5/22"
## [965] "10/5/21" "10/5/20" "10/4/22" "10/4/21"
## [969] "10/4/20" "10/31/22" "10/31/21" "10/31/20"
## [973] "10/30/22" "10/30/21" "10/30/20" "10/3/22"
## [977] "10/3/21" "10/3/20" "10/29/22" "10/29/21"
## [981] "10/29/20" "10/28/22" "10/28/21" "10/28/20"
## [985] "10/27/22" "10/27/21" "10/27/20" "10/26/22"
## [989] "10/26/21" "10/26/20" "10/25/22" "10/25/21"
## [993] "10/25/20" "10/24/22" "10/24/21" "10/24/20"
## [997] "10/23/22" "10/23/21" "10/23/20" "10/22/22"
## [1001] "10/22/21" "10/22/20" "10/21/22" "10/21/21"
## [1005] "10/21/20" "10/20/22" "10/20/21" "10/20/20"
## [1009] "10/2/22" "10/2/21" "10/2/20" "10/19/22"
## [1013] "10/19/21" "10/19/20" "10/18/22" "10/18/21"
## [1017] "10/18/20" "10/17/22" "10/17/21" "10/17/20"
## [1021] "10/16/22" "10/16/21" "10/16/20" "10/15/22"
## [1025] "10/15/21" "10/15/20" "10/14/22" "10/14/21"
## [1029] "10/14/20" "10/13/22" "10/13/21" "10/13/20"
## [1033] "10/12/22" "10/12/21" "10/12/20" "10/11/22"
## [1037] "10/11/21" "10/11/20" "10/10/22" "10/10/21"
## [1041] "10/10/20" "10/1/22" "10/1/21" "10/1/20"
## [1045] "1/9/23" "1/9/22" "1/9/21" "1/8/23"
## [1049] "1/8/22" "1/8/21" "1/7/23" "1/7/22"
## [1053] "1/7/21" "1/6/23" "1/6/22" "1/6/21"
## [1057] "1/5/23" "1/5/22" "1/5/21" "1/4/23"
## [1061] "1/4/22" "1/4/21" "1/31/23" "1/31/22"
## [1065] "1/31/21" "1/31/20" "1/30/23" "1/30/22"
## [1069] "1/30/21" "1/30/20" "1/3/23" "1/3/22"
## [1073] "1/3/21" "1/29/23" "1/29/22" "1/29/21"
## [1077] "1/29/20" "1/28/23" "1/28/22" "1/28/21"
## [1081] "1/28/20" "1/27/23" "1/27/22" "1/27/21"
## [1085] "1/27/20" "1/26/23" "1/26/22" "1/26/21"
## [1089] "1/26/20" "1/25/23" "1/25/22" "1/25/21"
## [1093] "1/25/20" "1/24/23" "1/24/22" "1/24/21"
## [1097] "1/24/20" "1/23/23" "1/23/22" "1/23/21"
## [1101] "1/23/20" "1/22/23" "1/22/22" "1/22/21"
## [1105] "1/22/20" "1/21/23" "1/21/22" "1/21/21"
## [1109] "1/20/23" "1/20/22" "1/20/21" "1/2/23"
## [1113] "1/2/22" "1/2/21" "1/19/23" "1/19/22"
## [1117] "1/19/21" "1/18/23" "1/18/22" "1/18/21"
## [1121] "1/17/23" "1/17/22" "1/17/21" "1/16/23"
## [1125] "1/16/22" "1/16/21" "1/15/23" "1/15/22"
## [1129] "1/15/21" "1/14/23" "1/14/22" "1/14/21"
## [1133] "1/13/23" "1/13/22" "1/13/21" "1/12/23"
## [1137] "1/12/22" "1/12/21" "1/11/23" "1/11/22"
## [1141] "1/11/21" "1/10/23" "1/10/22" "1/10/21"
## [1145] "1/1/23" "1/1/22" "1/1/21"head(confirmed_us)## # A tibble: 6 × 1,154
## UID iso2 iso3 code3 FIPS Admin2 Province_State Country_Region Lat
## <dbl> <chr> <chr> <dbl> <dbl> <chr> <chr> <chr> <dbl>
## 1 84001001 US USA 840 1001 Autauga Alabama US 32.5
## 2 84001003 US USA 840 1003 Baldwin Alabama US 30.7
## 3 84001005 US USA 840 1005 Barbour Alabama US 31.9
## 4 84001007 US USA 840 1007 Bibb Alabama US 33.0
## 5 84001009 US USA 840 1009 Blount Alabama US 34.0
## 6 84001011 US USA 840 1011 Bullock Alabama US 32.1
## # ℹ 1,145 more variables: Long_ <dbl>, Combined_Key <chr>, `1/22/20` <dbl>,
## # `1/23/20` <dbl>, `1/24/20` <dbl>, `1/25/20` <dbl>, `1/26/20` <dbl>,
## # `1/27/20` <dbl>, `1/28/20` <dbl>, `1/29/20` <dbl>, `1/30/20` <dbl>,
## # `1/31/20` <dbl>, `2/1/20` <dbl>, `2/2/20` <dbl>, `2/3/20` <dbl>,
## # `2/4/20` <dbl>, `2/5/20` <dbl>, `2/6/20` <dbl>, `2/7/20` <dbl>,
## # `2/8/20` <dbl>, `2/9/20` <dbl>, `2/10/20` <dbl>, `2/11/20` <dbl>,
## # `2/12/20` <dbl>, `2/13/20` <dbl>, `2/14/20` <dbl>, `2/15/20` <dbl>, …head(deaths_us)## # A tibble: 6 × 1,155
## UID iso2 iso3 code3 FIPS Admin2 Province_State Country_Region Lat
## <dbl> <chr> <chr> <dbl> <dbl> <chr> <chr> <chr> <dbl>
## 1 84001001 US USA 840 1001 Autauga Alabama US 32.5
## 2 84001003 US USA 840 1003 Baldwin Alabama US 30.7
## 3 84001005 US USA 840 1005 Barbour Alabama US 31.9
## 4 84001007 US USA 840 1007 Bibb Alabama US 33.0
## 5 84001009 US USA 840 1009 Blount Alabama US 34.0
## 6 84001011 US USA 840 1011 Bullock Alabama US 32.1
## # ℹ 1,146 more variables: Long_ <dbl>, Combined_Key <chr>, Population <dbl>,
## # `1/22/20` <dbl>, `1/23/20` <dbl>, `1/24/20` <dbl>, `1/25/20` <dbl>,
## # `1/26/20` <dbl>, `1/27/20` <dbl>, `1/28/20` <dbl>, `1/29/20` <dbl>,
## # `1/30/20` <dbl>, `1/31/20` <dbl>, `2/1/20` <dbl>, `2/2/20` <dbl>,
## # `2/3/20` <dbl>, `2/4/20` <dbl>, `2/5/20` <dbl>, `2/6/20` <dbl>,
## # `2/7/20` <dbl>, `2/8/20` <dbl>, `2/9/20` <dbl>, `2/10/20` <dbl>,
## # `2/11/20` <dbl>, `2/12/20` <dbl>, `2/13/20` <dbl>, `2/14/20` <dbl>, …cases <- confirmed_us %>%
pivot_longer(cols = -c(UID:Combined_Key), names_to = "date", values_to = "Cases")%>%
select(-c(iso2, iso3, code3, FIPS, UID, Country_Region))%>%
mutate(date = mdy(date))
summary(cases)## Admin2 Province_State Lat Long_
## Length:3819906 Length:3819906 Min. :-14.27 Min. :-174.16
## Class :character Class :character 1st Qu.: 33.90 1st Qu.: -97.81
## Mode :character Mode :character Median : 38.01 Median : -89.49
## Mean : 36.72 Mean : -88.64
## 3rd Qu.: 41.58 3rd Qu.: -82.31
## Max. : 69.31 Max. : 145.67
## Combined_Key date Cases
## Length:3819906 Min. :2020-01-22 Min. : -3073
## Class :character 1st Qu.:2020-11-02 1st Qu.: 330
## Mode :character Median :2021-08-15 Median : 2272
## Mean :2021-08-15 Mean : 14088
## 3rd Qu.:2022-05-28 3rd Qu.: 8159
## Max. :2023-03-09 Max. :3710586deaths <- deaths_us %>%
pivot_longer(cols = -c(UID:Population), names_to = "date", values_to = "deaths")%>%
select(-c(iso2, iso3, code3, FIPS, UID, Country_Region))%>%
mutate(date = mdy(date))
summary(deaths)## Admin2 Province_State Lat Long_
## Length:3819906 Length:3819906 Min. :-14.27 Min. :-174.16
## Class :character Class :character 1st Qu.: 33.90 1st Qu.: -97.81
## Mode :character Mode :character Median : 38.01 Median : -89.49
## Mean : 36.72 Mean : -88.64
## 3rd Qu.: 41.58 3rd Qu.: -82.31
## Max. : 69.31 Max. : 145.67
## Combined_Key Population date deaths
## Length:3819906 Min. : 0 Min. :2020-01-22 Min. : -82.0
## Class :character 1st Qu.: 9917 1st Qu.:2020-11-02 1st Qu.: 4.0
## Mode :character Median : 24892 Median :2021-08-15 Median : 37.0
## Mean : 99604 Mean :2021-08-15 Mean : 186.9
## 3rd Qu.: 64979 3rd Qu.:2022-05-28 3rd Qu.: 122.0
## Max. :10039107 Max. :2023-03-09 Max. :35545.0# For 'global_cases' df, make 'Province/State' and 'Country/Region' factors and pivot dates into rows
global_cases <- mutate_at(global_cases, vars('Province/State', 'Country/Region'), as.factor) %>%
pivot_longer(cols = -c('Province/State', 'Country/Region', 'Lat', 'Long'),
names_to = 'Date',
values_to = 'Cases') %>%
select(-c('Lat', 'Long'))
# For 'global_deaths' df, make 'Province/State' and 'Country/Region' factors and pivot dates into rows
global_deaths <- mutate_at(global_deaths, vars('Province/State', 'Country/Region'), as.factor) %>%
pivot_longer(cols = -c('Province/State', 'Country/Region', 'Lat', 'Long'),
names_to = 'Date',
values_to = 'Deaths') %>%
select(-c('Lat', 'Long'))
# Merge 'global_cases' df and 'global_deaths' df into 'global' df and rename columnsglobal <- global_cases %>%
full_join(global_deaths) %>%
rename(Country_Region = 'Country/Region',
Province_State = 'Province/State') %>%
mutate(Date = mdy(Date))## Joining with `by = join_by(`Province/State`, `Country/Region`, Date)`# Combine 'Province_State' and 'Country_Region' columns into one 'Combined_Key' column
global <- global %>%
unite('Combined_Key',
c(Province_State, Country_Region),
sep = ', ',
na.rm = TRUE,
remove = FALSE)
# Join 'global' df with global population lookup table df and remove unneeded columns
global <- global %>%
left_join(uid, by = c('Province_State', 'Country_Region')) %>%
select(-c(UID, FIPS)) %>%
select(Province_State, Country_Region, Date,
Cases, Deaths, Population, Combined_Key)global_cases_per_hundred <- global %>%
group_by(Country_Region, Population) %>%
summarize(Cases = max(Cases), Population = max(Population, na.rm = T)) %>%
mutate(Cases_per_hundred = (Cases/Population)*100) %>%
arrange(desc(Cases_per_hundred)) %>%
filter(Population > 0) %>%
select(Country_Region, Population, Cases, Cases_per_hundred) %>%
ungroup()global_cases_per_hundred <- global_cases_per_hundred %>%
group_by(Country_Region, Population) %>%
summarize(Cases = sum(Cases), Population = sum(Population)) %>%
summarize(Cases = max(Cases), Population = max(Population, na.rm = T)) %>%
mutate(Cases_per_hundred = (Cases/Population)*100) %>%
arrange(desc(Cases_per_hundred)) %>%
filter(Population > 0) %>%
select(Country_Region, Population, Cases, Cases_per_hundred) %>%
ungroup()global_deaths_per_hundred <- global %>%
group_by(Country_Region, Population) %>%
summarize(Deaths = max(Deaths), Population = max(Population, na.rm = T)) %>%
mutate(Deaths_per_hundred = (Deaths/Population)*100) %>%
arrange(desc(Deaths_per_hundred)) %>%
filter(Population > 0) %>%
select(Country_Region, Population, Deaths, Deaths_per_hundred) %>%
ungroup()
# Combine populations and death totals for countries with 'State_Province' factors
global_deaths_per_hundred <- global_deaths_per_hundred %>%
group_by(Country_Region, Population) %>%
summarize(Deaths = sum(Deaths), Population = sum(Population)) %>%
summarize(Deaths = max(Deaths), Population = max(Population, na.rm = T)) %>%
mutate(Deaths_per_hundred = (Deaths/Population)*100) %>%
arrange(desc(Deaths_per_hundred)) %>%
filter(Population > 0) %>%
select(Country_Region, Population, Deaths, Deaths_per_hundred) %>%
ungroup()US_cases <- mutate_at(US_cases, vars(Admin2, Province_State, Country_Region), as.factor) %>%
rename(County = 'Admin2') %>%
pivot_longer(cols = -(UID:Combined_Key),
names_to = 'Date',
values_to = 'Cases') %>%
filter(Cases >= 0) %>%
select(County:Cases) %>%
mutate(Date = mdy(Date)) %>%
select(-c(Lat, Long_))
# For 'US_deaths' df, create factors and pivot dates into rows, change 'Date' column to mdy
US_deaths <- mutate_at(US_deaths, vars(Admin2, Province_State, Country_Region), as.factor) %>%
rename(County = 'Admin2') %>%
pivot_longer(cols = -(UID:Population),
names_to = 'Date',
values_to = 'Deaths') %>%
filter(Deaths >= 0) %>%
select(County:Deaths) %>%
mutate(Date = mdy(Date)) %>%
select(-c(Lat, Long_))
# Merge 'US_cases' df and 'US_deaths' df into 'US' df
US <- US_cases %>%
full_join(US_deaths)## Joining with `by = join_by(County, Province_State, Country_Region,
## Combined_Key, Date)`# For 'US_by_state' df, calculate sums of 'Cases', 'Deaths', and 'Population' variables by US state
US_by_state <- US %>%
group_by(Province_State, Country_Region, Date) %>%
summarize(Cases = sum(Cases), Deaths = sum(Deaths), Population = sum(Population)) %>%
select(Province_State, Country_Region, Date, Cases, Deaths, Population) %>%
ungroup()
# For 'US_by_state_cases_deaths_per_day' df, calculate 'New_Cases' and 'New_Deaths' variables
US_by_state_cases_deaths_per_day <- US_by_state %>%
group_by(Province_State) %>%
mutate(New_Cases = Cases - lag(Cases),
New_Deaths = Deaths - lag(Deaths)) %>%
select(Province_State, Country_Region, Date, Cases, Deaths, Population,
New_Cases, New_Deaths) %>%
ungroup()index1 <- which(US_by_state_cases_deaths_per_day$New_Cases >= 0)
US_by_state_cases_deaths_per_day <- US_by_state_cases_deaths_per_day[index1,]
# Remove negative 'New_Deaths' values from 'US_by_state_cases_deaths_per_day' df
index2 <- which(US_by_state_cases_deaths_per_day$New_Deaths >= 0)
US_by_state_cases_deaths_per_day <- US_by_state_cases_deaths_per_day[index2,]
# Group 'US_by_state_cases_deaths_per_day' df by 'Province_State' and filter rows with population > 0
US_by_state_cases_deaths_per_day <- US_by_state_cases_deaths_per_day %>%
group_by(Province_State, Date) %>%
select(Province_State, Country_Region, Date, Cases, Deaths, Population,
New_Cases, New_Deaths) %>%
filter(Population > 0) %>%
ungroup()
# Group by 'Province_State', record max in 'Cases' variable, and calculate 'Cases_per_hundred' variable
US_by_state_cases_per_hundred <- US_by_state %>%
group_by(Province_State, Population) %>%
summarize(Cases = max(Cases)) %>%
mutate(Cases_per_hundred = (Cases/Population)*100) %>%
arrange(desc(Cases_per_hundred)) %>%
filter(Population > 0) %>%
select(Province_State, Population, Cases, Cases_per_hundred) %>%
ungroup()
# Group by 'Province_State', record max in 'Deaths' variable, and calculate 'Deaths_per_hundred' variable
US_by_state_deaths_per_hundred <- US_by_state %>%
group_by(Province_State, Population) %>%
summarize(Deaths = max(Deaths)) %>%
mutate(Deaths_per_hundred = (Deaths/Population)*100) %>%
arrange(desc(Deaths_per_hundred)) %>%
filter(Population > 0) %>%
select(Province_State, Population, Deaths, Deaths_per_hundred) %>%
ungroup()US_vaccinations <- mutate_at(US_vaccinations, vars('Province_State'), as.factor)
# Create 'US_by_state_vaccinations_per_hundred' df holding max vaccination rates per US state
US_by_state_vaccinations_per_hundred <- US_vaccinations %>%
group_by(Province_State) %>%
mutate(Province_State = fct_recode(Province_State,
"New York" = "New York State")) %>%
summarize(people_fully_vaccinated_per_hundred = max(people_fully_vaccinated_per_hundred, na.rm = T),
total_vaccinations_per_hundred = max(total_vaccinations_per_hundred, na.rm = T),
people_vaccinated_per_hundred = max(people_vaccinated_per_hundred, na.rm = T),
distributed_per_hundred = max(distributed_per_hundred, na.rm = T),
total_boosters_per_hundred = max(total_boosters_per_hundred, na.rm = T))
# Merge 'US_by_state_deaths_per_hundred' df and 'US_by_state_vaccinations_per_hundred' df
US_by_state_deaths_vaccinations_per_hundred <- US_by_state_deaths_per_hundred %>%
full_join(US_by_state_vaccinations_per_hundred) %>%
filter(Population > 0)## Joining with `by = join_by(Province_State)`Step 3: Visualization
On this plot i used a dot plot to reduce clutter and make it easier to compare values.
ggplot(global_cases_per_hundred, aes(x = reorder(Country_Region, +Cases_per_hundred), y = Cases_per_hundred)) +
geom_point(aes(color = Cases_per_hundred), size = 3) +
scale_color_viridis_c(option = "magma") +
labs(x = "Country", y = "# COVID-19 Cases per Hundred People", title = "Global COVID-19 Cases per Hundred People by Country") +
theme(axis.text.x = element_text(angle = 90, hjust = 1, size = 6))
## Treemap can provide a visual representation of the data in a
hierachical manner. A treemap could show each country as a rectangle,
with the size of the rectangle representing the number of cases.
Countries with more cases would have larger rectangles. If you add
another layer, like continents, the treemap would first show rectangles
for each continent, and within each continent, rectangles for each
country. In this project cases per Hundred People it is a normalized
metric that shows the number of COVID-19 cases per hundred people in the
population. It helps to compare the impact of COVID-19 across countries
with different population sizes.One might ask why Latvia smaller country
has a bigger rectangle than the US? The answer is because the treemap is
visualizing the normalized metric (cases per hundred people) rather than
the absolute number of cases. I have added for your understanding the
absolute number case plot right after this one.
library(treemapify)
ggplot(global_cases_per_hundred, aes(area = Cases_per_hundred, fill = Cases_per_hundred, label = Country_Region)) +
geom_treemap() +
geom_treemap_text(colour = "white", place = "centre", grow = TRUE) +
scale_fill_viridis_c(option = "magma") +
labs(title = "Global COVID-19 Cases per Hundred People by Country")
ggplot(global_cases_per_hundred, aes(area = Cases, fill = Cases_per_hundred, label = Country_Region)) +
geom_treemap() +
geom_treemap_text(colour = "white", place = "centre", grow = TRUE) +
scale_fill_viridis_c(option = "magma") +
labs(title = "Global COVID-19 Cases by Country")
Using plotly pacakge can make our plot interactive and alowing to zoom in and out.
library(plotly)
p <- ggplot(US_by_state_cases_deaths_per_day, aes(x = Date, y = New_Deaths, color = Province_State)) +
geom_line() +
facet_wrap(~Province_State) +
labs(x = "", y = "# of COVID-19 Deaths", title = "COVID-19 Deaths in the US", subtitle = "By State/Territory") +
theme(legend.position = "none")
ggplotly(p)Here I am trying to build different model.
##We will build a model based on cases per 1000 and deaths per 1000, output the summary, then add the predictions to the Mass. county data.
ggplot(data = US_by_state_deaths_vaccinations_per_hundred, aes(x = people_fully_vaccinated_per_hundred,
y = Deaths_per_hundred,
color = Province_State, # Color by state
label = Province_State)) +
geom_point(size = .4) +
geom_text(size = 1.7, vjust = .5, hjust = -.1) +
geom_smooth(method = "lm", se = FALSE) + # Add linear regression line without confidence interval
labs(x = "# fully vaccinated per hundred people",
y = "# of COVID-19 deaths per hundred people",
title = "US COVID-19 Deaths / # of Fully Vaccinated People - Linear Model",
subtitle = "By US State/Territory") +
theme_minimal() # Use a minimal theme for better readability## `geom_smooth()` using formula = 'y ~ x'
cases <- confirmed_us %>%
pivot_longer(cols = -c(UID:Combined_Key), names_to = "date", values_to = "Cases")%>%
select(-c(iso2, iso3, code3, FIPS, UID, Country_Region))%>%
mutate(date = mdy(date))
summary(cases)## Admin2 Province_State Lat Long_
## Length:3819906 Length:3819906 Min. :-14.27 Min. :-174.16
## Class :character Class :character 1st Qu.: 33.90 1st Qu.: -97.81
## Mode :character Mode :character Median : 38.01 Median : -89.49
## Mean : 36.72 Mean : -88.64
## 3rd Qu.: 41.58 3rd Qu.: -82.31
## Max. : 69.31 Max. : 145.67
## Combined_Key date Cases
## Length:3819906 Min. :2020-01-22 Min. : -3073
## Class :character 1st Qu.:2020-11-02 1st Qu.: 330
## Mode :character Median :2021-08-15 Median : 2272
## Mean :2021-08-15 Mean : 14088
## 3rd Qu.:2022-05-28 3rd Qu.: 8159
## Max. :2023-03-09 Max. :3710586deaths <- deaths_us %>%
pivot_longer(cols = -c(UID:Population), names_to = "date", values_to = "deaths")%>%
select(-c(iso2, iso3, code3, FIPS, UID, Country_Region))%>%
mutate(date = mdy(date))
summary(deaths)## Admin2 Province_State Lat Long_
## Length:3819906 Length:3819906 Min. :-14.27 Min. :-174.16
## Class :character Class :character 1st Qu.: 33.90 1st Qu.: -97.81
## Mode :character Mode :character Median : 38.01 Median : -89.49
## Mean : 36.72 Mean : -88.64
## 3rd Qu.: 41.58 3rd Qu.: -82.31
## Max. : 69.31 Max. : 145.67
## Combined_Key Population date deaths
## Length:3819906 Min. : 0 Min. :2020-01-22 Min. : -82.0
## Class :character 1st Qu.: 9917 1st Qu.:2020-11-02 1st Qu.: 4.0
## Mode :character Median : 24892 Median :2021-08-15 Median : 37.0
## Mean : 99604 Mean :2021-08-15 Mean : 186.9
## 3rd Qu.: 64979 3rd Qu.:2022-05-28 3rd Qu.: 122.0
## Max. :10039107 Max. :2023-03-09 Max. :35545.0Mass_Cases <- cases %>%
filter(Province_State == "Massachusetts")#%>%
# group_by(Admin2)
Mass_Deaths <- deaths %>%
filter(Province_State == "Massachusetts")#%>%
# group_by(Admin2)
All_Mass <- Mass_Cases %>%
full_join(Mass_Deaths)## Joining with `by = join_by(Admin2, Province_State, Lat, Long_, Combined_Key,
## date)`Mass <- All_Mass %>%
mutate(deaths_per_k= deaths * 1000 / Population, cases_per_k= Cases * 1000 / Population, month_year = format_ISO8601(date, precision="ym"), Lng = Long_, month= month(date))%>%filter(Population > 0)
summary(Mass)## Admin2 Province_State Lat Long_
## Length:16002 Length:16002 Min. :41.29 Min. :-73.21
## Class :character Class :character 1st Qu.:41.79 1st Qu.:-72.59
## Mode :character Mode :character Median :42.24 Median :-71.16
## Mean :42.11 Mean :-71.47
## 3rd Qu.:42.37 3rd Qu.:-70.81
## Max. :42.67 Max. :-70.09
## Combined_Key date Cases Population
## Length:16002 Min. :2020-01-22 Min. : 0 Min. : 11399
## Class :character 1st Qu.:2020-11-02 1st Qu.: 1475 1st Qu.: 124944
## Mode :character Median :2021-08-15 Median : 23197 Median : 493787
## Mean :2021-08-15 Mean : 65074 Mean : 492322
## 3rd Qu.:2022-05-28 3rd Qu.:104131 3rd Qu.: 789034
## Max. :2023-03-09 Max. :437431 Max. :1611699
## deaths deaths_per_k cases_per_k month_year
## Min. : 0 Min. :0.0000 Min. : 0.000 Length:16002
## 1st Qu.: 77 1st Qu.:0.7267 1st Qu.: 7.139 Class :character
## Median : 683 Median :1.8719 Median : 75.010 Mode :character
## Mean :1028 Mean :1.7397 Mean :106.850
## 3rd Qu.:1794 3rd Qu.:2.6503 3rd Qu.:203.338
## Max. :4822 Max. :4.5843 Max. :368.944
## Lng month
## Min. :-73.21 Min. : 1.000
## 1st Qu.:-72.59 1st Qu.: 3.000
## Median :-71.16 Median : 6.000
## Mean :-71.47 Mean : 6.335
## 3rd Qu.:-70.81 3rd Qu.: 9.000
## Max. :-70.09 Max. :12.000head(Mass)## # A tibble: 6 × 14
## Admin2 Province_State Lat Long_ Combined_Key date Cases Population
## <chr> <chr> <dbl> <dbl> <chr> <date> <dbl> <dbl>
## 1 Barnstable Massachusetts 41.7 -70.3 Barnstable,… 2020-01-22 0 212990
## 2 Barnstable Massachusetts 41.7 -70.3 Barnstable,… 2020-01-23 0 212990
## 3 Barnstable Massachusetts 41.7 -70.3 Barnstable,… 2020-01-24 0 212990
## 4 Barnstable Massachusetts 41.7 -70.3 Barnstable,… 2020-01-25 0 212990
## 5 Barnstable Massachusetts 41.7 -70.3 Barnstable,… 2020-01-26 0 212990
## 6 Barnstable Massachusetts 41.7 -70.3 Barnstable,… 2020-01-27 0 212990
## # ℹ 6 more variables: deaths <dbl>, deaths_per_k <dbl>, cases_per_k <dbl>,
## # month_year <chr>, Lng <dbl>, month <dbl>'Deaths & Population: ' ## [1] "Deaths & Population: "cor(Mass$deaths, Mass$Population)## [1] 0.7976191'Cases & Population: '## [1] "Cases & Population: "cor(Mass$Cases, Mass$Population)## [1] 0.6327786'Cases & Deaths: '## [1] "Cases & Deaths: "cor(Mass$Cases, Mass$deaths)## [1] 0.924681'Cases/1000 & Deaths/1000: '## [1] "Cases/1000 & Deaths/1000: "cor(Mass$cases_per_k, Mass$deaths_per_k)## [1] 0.91824Mass_Model <- lm(cases_per_k ~ deaths_per_k, Mass)
summary(Mass_Model)##
## Call:
## lm(formula = cases_per_k ~ deaths_per_k, data = Mass)
##
## Residuals:
## Min 1Q Median 3Q Max
## -79.130 -38.676 6.675 30.785 146.494
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -30.7845 0.5764 -53.41 <2e-16 ***
## deaths_per_k 79.1149 0.2698 293.29 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 42.33 on 16000 degrees of freedom
## Multiple R-squared: 0.8432, Adjusted R-squared: 0.8432
## F-statistic: 8.602e+04 on 1 and 16000 DF, p-value: < 2.2e-16Mass_Pred <- Mass %>%
mutate(Prediction = predict(Mass_Model))Mass_Model <- lm(Deaths_per_hundred ~ people_fully_vaccinated_per_hundred,
data = US_by_state_deaths_vaccinations_per_hundred)
summary(Mass_Model)##
## Call:
## lm(formula = Deaths_per_hundred ~ people_fully_vaccinated_per_hundred,
## data = US_by_state_deaths_vaccinations_per_hundred)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.261032 -0.049752 0.007771 0.051880 0.149193
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.659862 0.076443 8.632 9.54e-12 ***
## people_fully_vaccinated_per_hundred -0.004994 0.001088 -4.590 2.68e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.08772 on 54 degrees of freedom
## Multiple R-squared: 0.2807, Adjusted R-squared: 0.2674
## F-statistic: 21.07 on 1 and 54 DF, p-value: 2.68e-05US_by_state_deaths_vaccinations_per_hundred$predicted_deaths <- predict(Mass_Model, US_by_state_deaths_vaccinations_per_hundred)
ggplot(data = US_by_state_deaths_vaccinations_per_hundred, aes(x = predicted_deaths, y = Deaths_per_hundred, label = Province_State)) +
geom_point(size = .4) +
geom_text(size = 1.7, vjust = .5, hjust = -.1) +
geom_abline(slope = 1, intercept = 0, color = "red") +
labs(x = "Predicted # of COVID-19 deaths per hundred people",
y = "Observed # of COVID-19 deaths per hundred people",
title = "Observed vs Predicted COVID-19 Deaths",
subtitle = "Multiple Regression Model")
##Using a bar plot (geom_col()) to visualize predictions for individual counties can make sense, especially if you want to compare the number of cases across different counties on specific dates. However, there are a few considerations to ensure the plot is clear and informative:
ggplot(Mass_Pred, aes(x = date, y = Cases, fill = Admin2)) +
geom_col(position = "dodge") +
facet_wrap(~ Admin2, scales = "free_y") +
labs(x = "Date", y = "Number of Cases", title = "COVID-19 Cases by County") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
##Using a bar plot (geom_col()) to visualize predictions for individual
counties can make sense, especially if you want to compare the number of
cases across different counties on specific dates.
ggplot(Mass_Pred, aes(x=date, y=Cases, fill=Admin2)) +
geom_col(position = "dodge")
ggplot(Mass_Pred, aes(x="", y=Cases, fill=Admin2)) +
geom_bar(stat="identity", width=1) +
coord_polar(theta="y") +
theme_void()
Mass_Model <- lm(cases_per_k ~ deaths_per_k, Mass)
summary(Mass_Model)##
## Call:
## lm(formula = cases_per_k ~ deaths_per_k, data = Mass)
##
## Residuals:
## Min 1Q Median 3Q Max
## -79.130 -38.676 6.675 30.785 146.494
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -30.7845 0.5764 -53.41 <2e-16 ***
## deaths_per_k 79.1149 0.2698 293.29 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 42.33 on 16000 degrees of freedom
## Multiple R-squared: 0.8432, Adjusted R-squared: 0.8432
## F-statistic: 8.602e+04 on 1 and 16000 DF, p-value: < 2.2e-16Mass_Pred <- Mass %>%
mutate(Prediction = predict(Mass_Model))Mass_County <- Mass %>%
group_by(Admin2)%>%
summarize(Max_Deaths=max(deaths), Total_Deaths = sum(deaths),Max_Cases = max(Cases), Total_Cases = sum(Cases), Population = max(Population))
summary(Mass_County)## Admin2 Max_Deaths Total_Deaths Max_Cases
## Length:14 Min. : 0.0 Min. : 0 Min. : 0
## Class :character 1st Qu.: 459.8 1st Qu.: 276102 1st Qu.: 36522
## Mode :character Median :2050.0 Median :1382960 Median :157826
## Mean :1735.9 Mean :1175195 Mean :143892
## 3rd Qu.:2498.8 3rd Qu.:1665867 3rd Qu.:226117
## Max. :4822.0 Max. :3378924 Max. :437431
## Total_Cases Population
## Min. : 0 Min. : 11399
## 1st Qu.: 15948862 1st Qu.: 133916
## Median : 81161396 Median : 493787
## Mean : 74379791 Mean : 492322
## 3rd Qu.:118428435 3rd Qu.: 768469
## Max. :220834357 Max. :1611699head(Mass_County)## # A tibble: 6 × 6
## Admin2 Max_Deaths Total_Deaths Max_Cases Total_Cases Population
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Barnstable 785 447242 49617 23514236 212990
## 2 Berkshire 480 276606 35456 15223406 124944
## 3 Bristol 2555 1619263 182344 98339486 565217
## 4 Dukes 0 0 0 0 17332
## 5 Essex 3272 2235421 256987 140031284 789034
## 6 Franklin 198 108143 14736 6453660 70180massachusetts_data <- US_by_state_deaths_vaccinations_per_hundred %>%
filter(Province_State == "Massachusetts")To assess whether the data is unbiased, I would need to consider the data collection methods, potential confounding variables, and whether the data accurately represents the population without systematic errors.
ggplot(data = massachusetts_data, aes(x = people_fully_vaccinated_per_hundred,
y = Deaths_per_hundred,
label = Province_State)) +
geom_point(size = .4) +
geom_text(size = 1.7, vjust = .5, hjust = -.1) +
geom_smooth(method = "lm") +
labs(x = "# fully vaccinated per hundred people",
y = "# of COVID-19 deaths per hundred people",
title = "COVID-19 Deaths vs. Fully Vaccinated People in Massachusetts",
subtitle = "Linear Model") +
theme_minimal()## `geom_smooth()` using formula = 'y ~ x'
Conclusion
##Our prediction managed to show multiple regression and linear regression. Regression is a powerful tool for predictive analytics. Linear is fundamental and widely used in predictive analytics. Linear regression relies on several assumptions, including linearity, independence, homoscedasticity (constant variance of errors), and normality of errors.
Step 5
Personal Bias
To assess whether the data is unbiased, you would need to consider the data collection methods, potential confounding variables, and whether the data accurately represents the population without systematic errors. A dot in the middle of the plot doesn’t necessarily prove that the data is unbiased. It simply represents a specific observation where the number of fully vaccinated people and the number of deaths fall around the middle range of your dataset.